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1. REAL-TIME BIG DATA ANALYTICAL ARCHITECTURE
FOR REMOTE SENSING APPLICATION
ABSTRACT
The assets of remote senses digital world daily generate massive volume of real-time data
(mainly referred to the term “Big Data”), where insight information has a potential significance
if collected and aggregated effectively. In today’s era, there is a great deal added to real-time
remote sensing Big Data than it seems at first, and extracting the useful information in an
efficient manner leads a system toward a major computational challenges, such as to analyze,
aggregate, and store, where data are remotely collected. Keeping in view the above mentioned
factors, there is a need for designing a system architecture that welcomes both realtime, as well
as offline data processing. Therefore, in this paper, we propose real-time Big Data analytical
architecture for remote sensing satellite application. The proposed architecture comprises three
main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit
(DPU); and 3) data analysis decision unit (DADU). First, RSDU acquires data from the satellite
and sends this data to the Base Station, where initial processing takes place. Second, DPU plays
a vital role in architecture for efficient processing of real-time Big Data by providing filtration,
load balancing, and parallel processing. Third, DADU is the upper layer unit of the proposed
architecture, which is responsible for compilation, storage of the results, and generation of
decision based on the results received from DPU. The proposed architecture has the capability of
dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently
analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the
proposed architecture has the capability of storing incoming raw data to perform offline analysis
on largely stored dumps, when required. Finally, a detailed analysis of remotely sensed earth
observatory Big Data for land and sea area are provided using Hadoop. In addition, various
algorithms are proposed for each level of RSDU, DPU, and DADU to detect land as well as sea
area to elaborate the working of an architecture.